Based on these studies, we hypothesize that a hybrid approach that works with both information as presented in k-space domain and image domain (such as the proposal of [1]}) can improve MR CS reconstruction. In this work, we propose a model that consists of a cascade of a k-space domain network and an image domain network connected by the magnitude of the iDFT operation. Our model does not need to learn the domain transform, which essentially reduces our model parameter complexity to \(O(N^2)\) . For a \(256\times 256\)  input image, the number of learned parameters is reduced by a factor of \(10^3\)  for our method compared to [1]} (\(\approx 10^{7}\)  parameters). Our proposed method takes advantage of information as presented in k-space and image domain, as opposed to other image domain only approaches [3]}, [4]}, [5]}, [6]}, [7]}. K-Space and image domain information are equivalent, because they are related by a global linear transformation. Operating in both domains with non-linear methods, however, can potentially be advantageous and improve the network learning.
